Results 11 to 20 of about 4,498 (200)
Neuroevolution of self-interpretable agents [PDF]
Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020 To appear at the Genetic and Evolutionary Computation Conference (GECCO 2020) as a full ...
Yujin Tang, Duong Nguyen, David Ha
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Hetero-Dimensional Multitask Neuroevolution for Chaotic Time Series Prediction
IEEE Access, 2020 Chaotic time series prediction has important research and application value, and neural network-based prediction methods have problems such as low accuracy and difficulty in determining the number of nodes in the hidden layer.
Daoqing Zhang, Mingyan Jiang
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Neuroevolutionary reinforcing learning of neural networks
Системный анализ и прикладная информатика, 2022 The article presents the results of combining 4 different types of neural network learning: evolutionary, reinforcing, deep and extrapolating. The last two are used as the primary method for reducing the dimension of the input signal of the system and ...
Y. A. Bury, D. I. Samal
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Neuroevolution for RTS Micro [PDF]
2018 IEEE Conference on Computational Intelligence and Games (CIG), 2018 This paper uses neuroevolution of augmenting topologies to evolve control tactics for groups of units in real-time strategy games. In such games, players build economies to generate armies composed of multiple types of units with different attack and movement characteristics to combat each other.
Aavaas Gajurel +3 more
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Monarch Butterfly Optimization Based Convolutional Neural Network Design
Mathematics, 2020 Convolutional neural networks have a broad spectrum of practical applications in computer vision. Currently, much of the data come from images, and it is crucial to have an efficient technique for processing these large amounts of data.
Nebojsa Bacanin +4 more
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An Adaptive Island Model of Population for Neuroevolutionary Ship Handling
Polish Maritime Research, 2022 This study presents a method for the dynamic value assignment of evolutionary parameters to accelerate, automate and generalise the neuroevolutionary method of ship handling for different navigational tasks and in different environmental conditions.
Łącki Mirosław
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Data-efficient Neuroevolution with Kernel-Based Surrogate Models [PDF]
, 2018 Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so far resisted the application of these techniques because it requires the ...
Lehman J +4 more
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Scholarpedia, 2013 Neuroevolution is a machine learning technique that applies evolutionary algorithms to construct artificial neural networks, taking inspiration from the evolution of biological nervous systems in nature. Compared to other neural network learning methods, neuroevolution is highly general; it allows learning without explicit targets, with only sparse ...
Lehman, Joel, Miikkulainen, Risto
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Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022 Evolutionary computation has been shown to be a highly effective method for training neural networks, particularly when employed at scale on CPU clusters. Recent work have also showcased their effectiveness on hardware accelerators, such as GPUs, but so far such demonstrations are tailored for very specific tasks, limiting applicability to other ...
Yujin Tang, Yingtao Tian, David Ha
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A NEAT Visualisation of Neuroevolution Trajectories [PDF]
, 2021 NeuroEvolution of Augmenting Topologies (NEAT) is a system for evolving neural network topologies along with weights that has proven highly effective and adaptable for solving challenging reinforcement learning tasks. This paper analyses NEAT through the lens of Search Trajectory Networks (STNs), a recently proposed visual approach to study the ...
Stefano Sarti, Gabriela Ochoa
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